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Running
on
T4
| # -------------------------------------------------------- | |
| # SiamMask | |
| # Licensed under The MIT License | |
| # Written by Qiang Wang (wangqiang2015 at ia.ac.cn) | |
| # -------------------------------------------------------- | |
| import glob | |
| from tools.test import * | |
| parser = argparse.ArgumentParser(description='PyTorch Tracking Demo') | |
| parser.add_argument('--resume', default='', type=str, required=True, | |
| metavar='PATH',help='path to latest checkpoint (default: none)') | |
| parser.add_argument('--config', dest='config', default='config_davis.json', | |
| help='hyper-parameter of SiamMask in json format') | |
| parser.add_argument('--base_path', default='../../data/tennis', help='datasets') | |
| parser.add_argument('--cpu', action='store_true', help='cpu mode') | |
| args = parser.parse_args() | |
| if __name__ == '__main__': | |
| # Setup device | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| torch.backends.cudnn.benchmark = True | |
| # Setup Model | |
| cfg = load_config(args) | |
| from custom import Custom | |
| siammask = Custom(anchors=cfg['anchors']) | |
| if args.resume: | |
| assert isfile(args.resume), 'Please download {} first.'.format(args.resume) | |
| siammask = load_pretrain(siammask, args.resume) | |
| siammask.eval().to(device) | |
| # Parse Image file | |
| img_files = sorted(glob.glob(join(args.base_path, '*.jp*'))) | |
| ims = [cv2.imread(imf) for imf in img_files] | |
| # Select ROI | |
| cv2.namedWindow("SiamMask", cv2.WND_PROP_FULLSCREEN) | |
| # cv2.setWindowProperty("SiamMask", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN) | |
| try: | |
| init_rect = cv2.selectROI('SiamMask', ims[0], False, False) | |
| x, y, w, h = init_rect | |
| except: | |
| exit() | |
| toc = 0 | |
| for f, im in enumerate(ims): | |
| tic = cv2.getTickCount() | |
| if f == 0: # init | |
| target_pos = np.array([x + w / 2, y + h / 2]) | |
| target_sz = np.array([w, h]) | |
| state = siamese_init(im, target_pos, target_sz, siammask, cfg['hp'], device=device) # init tracker | |
| elif f > 0: # tracking | |
| state = siamese_track(state, im, mask_enable=True, refine_enable=True, device=device) # track | |
| location = state['ploygon'].flatten() | |
| mask = state['mask'] > state['p'].seg_thr | |
| im[:, :, 2] = (mask > 0) * 255 + (mask == 0) * im[:, :, 2] | |
| cv2.polylines(im, [np.int0(location).reshape((-1, 1, 2))], True, (0, 255, 0), 3) | |
| cv2.imshow('SiamMask', im) | |
| key = cv2.waitKey(1) | |
| if key > 0: | |
| break | |
| toc += cv2.getTickCount() - tic | |
| toc /= cv2.getTickFrequency() | |
| fps = f / toc | |
| print('SiamMask Time: {:02.1f}s Speed: {:3.1f}fps (with visulization!)'.format(toc, fps)) | |